Comprehensive Design Analysis of Economical E-Bike Charger with IoT-Empowered System for Real-Time Parameter Monitoring

Abstract

The demand for electrically powered transportation is increasing exponentially due to high fuel prices and global environmental issues. The use of electric bikes is increasing rapidly within urban mobility. The current E-bike chargers are expensive and fail to provide proper authentication, real-time monitoring, parameter analysis, health maintenance alerts, etc. To meet future demand, the paper presents detailed design procedures and experimental analysis of smart, user-friendly, economical, and green charging solutions for electric bikes. The research provides an IoT-based cheap charging facility for different workplaces, organizations, and highway rest areas. Real-time pricing and parameters are sensed using an IoT module and observed online with a monitoring interface via the ThingSpeak platform. The parameters are visualized using both simulation and hardware analysis. The detailed power converter and high-frequency transformer design procedure with mathematical equations are presented in the article. The proposed design improves reliability, security, timely maintenance, and system health conditions. The research provides economical off-board charging stations with smart interfaces that can be accessed by users or service providers through the data cloud. The low-cost smart charging stations promote the use of electric vehicles and decrease the charging anxiety of users. The proposed scheme reduces emissions problems, improves the air quality index, and facilitates people with affordable and reliable transportation.

This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

1. Introduction

Worldwide environmental contaminations have become a big ecological and health problem due to industrial applications, transportation, and strong oil dependence for electrical energy generation. Additionally, related to dependence on oil as a main source of energy, it is important to consider the fact that oil is a nonrenewable source, which is becoming exhausted worldwide. In the last 50 years, road transportation through petrol and diesel has been one of the major sources of CO₂ emissions and it degrades the air quality level below European Union (EU) standards. Electric power must become much less dependent on fossil fuels and transportation must turn out to be more electric to lower carbon emissions and defend the weather change. For this purpose, it is a need of this era to take a step forward to neutralize the climate. So, electric vehicles (EVs) can promote this factor to achieve the required goal [1, 2]. However, in the coming days, EVs are powered by rechargeable batteries and classified as green technology vehicles to promote green energy, which will replace the internal combustion engine (ICE) [3–5].

Modern cities place a strong emphasis on low-pollution transportation systems. According to the EV30@30 campaign, many countries have decided to reach a target of 30% of EVs’ sales shares by 2030 [6]. Finland has decided to reduce 50% of greenhouse gases from the transport sector by 2030. Similarly, Sweden, Norway, and Mexico have decided to reduce 70%, 40%, and 22% of greenhouse gases from the transport sector by incorporating EVs. Electric vehicles (EV) had a strong development for different required applications such as cars, scooters, bicycles, and electrical wheelchairs among others. The use of electric bikes (E-bikes) is trending worldwide due to their lightweight, low cost, green, and compactness [7, 8]. These types of EVs are typically driven by stored electrical energy. Increasing the accessibility of charging stations is anticipated to grow purchases of electric vehicles. To address the modern inadequate charging infrastructure for electric-powered automobiles, major entities should adopt commercial enterprise fashions for electric vehicle charging stations [9–11]. However, one of the main EV problems is the short driving range due to the energy storage capacity and the very least number of charging stations available to complete this need. Currently, available charging stations are inefficient and much less reliable due to a lack of smart features. They also failed to provide proper parameter information to improve the charging infrastructure and E-bike performance to users or service providers.

In the EV charging system, optimum power and energy consumption needs to be maintained. An energy management system (EMS) helps control the consumption of energy so that the battery charge can last for longer distances. The EMS improves the life of the battery by maintaining thermal stability as well [12]. We need to incorporate advanced technology such as V2G, smart grid, and smart charging techniques to make power transfer more balanced and efficient [13]. The demand for EVs and charging systems will grow in the future. Existing infrastructure such as parking lots, existing fuel pumps, etc., should be developed into charging stations to reduce expenditure and help increase profit. Moreover, renewable energy such as PV charging sources has a better environmental impact and on the life cycle assessment (LCA) as well [14]. Greenhouse gas emissions in manufacturing EV batteries can be reduced by using renewable-based energy sources and developing the recycling industry so that EVs can be properly recycled [15].

The block scheme of smart, green, and IoT (Internet of Things)-based off-board electric bike (E-bike) charging station is presented in Figure 1, which provides green and cheap electric power to charge electric bikes. The layout shows the integration of the AC grid and green power sources like photovoltaic (PV), wind energy, etc. The power converter regulates, optimizes, and synchronizes the renewable sources and fed to the AC bus. The IoT-based charging system consists of power electronic circuitry to effectively charge and monitor the E-bike. The charging station allows users to charge their electric bikes in the most convenient way possible. The charging station includes renewable integration which reduces the power load burden on local grid stations and decreases charging price [16, 17]. Smart IoT-based system provides the facility to monitor bike battery parameter using mobile/PC application [18], which includes the battery state of charge (SoC), estimated charging time, cost of charging Rs/kWh, and thermal and health conditions.

[figure(s) omitted; refer to PDF]

Switch-mode power converters are an essential part of modern electronics. The switch mode power supply (SMPS)-based efficient DC-DC converter provides the optimum charging current and voltage level to the E-bike battery. IoT offers easier maintenance and management of charging devices to the user. IoT implementation provides numerous features and advancements to EV charging infrastructure and uses it as a core design of information and communication technology. It will make remarkable improvements in machine-to-machine communications and will improve real-time two-way communications to meet the needs of security, quality, and authenticity of service. The availability of charging stations promotes the use of electric bikes and decreases the charging anxiety of users. Typical E-bike batteries take 3 to 6 hours to recharge depending upon the capacity. The best way to install E-bike charging infrastructure is within different organizations to support electric bike users.

The main objective of the research is to present a detailed design procedure and hardware implementation of the novel economical smart E-bike charging station. IoT-based smart monitoring systems observe related parameters of charging stations that will improve the reliability, security, and performance of electric bikes. The research provides cheap and compatible off-board charging stations with multiple charging ports with smart interfaces that can be accessed by users or service providers through the data cloud. Renewable integration further improves environmental problems. Real-time monitoring setup demonstrates the charging cost, charging voltages and current, battery health, SoC, and fault diagnosis through a parameter observing system. The proposed prototype model provides numerous advantages for electric bike users and charging service providers like compatibility, availability, cheap infrastructure, smart features, promotion of business opportunities, and degradation of oil import bills. The availability of charging stations promotes the use of electric vehicles and decreases the charging anxiety of users. The development of a public economic charging station is a key element for promoting E-bikes. That will dramatically reduce emissions problems, improve the air quality index, and facilitate people with affordable and reliable transportation.

Sections 2 and 3 explain the importance of a smart E-bike charging system. Section 4 discusses the comprehensive design procedure of a smart E-bike charger. The section explains the complete mathematical equation for the power converter scheme along with a step-by-step design procedure of a high-frequency transformer and an IoT module. The Simscape (MATLAB) physical simulation modeling is performed in Section 5.1 to authenticate the design calculations. Section 5 presents the experimental implementation results of an IoT-based E-bike charger with a real-time parameter observing interface using the ThingSpeak platform. Section 5.3 discusses the economic aspect and comparison of the proposed scheme with different market-available chargers.

2. E-Bike Transport Trend and Charging Station

An electric charging station, also called an EV charging station, is an element in an infrastructure that supplies electric energy for the recharging of plug-in electric vehicles. The electric bike charging station is a facility to charge E-bikes and provides maximum convenience to the user, especially for daily intracity traveling [19]. Increasing the accessibility of charging stations is anticipated to grow purchases of electric vehicles. E-bikes have many benefits, such as being lightweight, cost-effective, less maintenance, cheap recharging, and pollution-free. Due to these benefits, e-bikes are becoming popular nowadays in many countries. The global market trend of electric bikes is illustrated in Figure 2. Electric bike chargers are in higher demand because of the popularity of electric bikes. It is an essential component that allows users of electric bikes to charge the bikes quickly and safely. Depending on the electric bike’s charging needs, electric bike chargers come in a variety of sizes and forms. While some chargers are made to be used at home or in the workplace, others are made to be used in public areas like parks, parking lots, and urban cores.

[figure(s) omitted; refer to PDF]

Mishra et al. [21] studied different E-bike charging trends and showed that mainly charging stations of bikes emphasize grid energy due to the low battery capacities of E-bike. Multilevel charging topology for E-mobility devices (electric-drive vehicles) is present in [22]. Researchers [23, 24] proposed inductive and wireless charging mechanisms of e-mobilities, respectively. The impact of power quality on solar-based E-bike charging systems is presented in [25]. In [26], Chandra Mouli and his team investigate DC, AC, and wireless technologies for E-bike charging stations. The DC charging level is still specified by the manufacturer, and no proper standard is present currently, but the typical voltage level for batteries is 24 V, 36 V, and 48 V. In short, electric bike charging stations are essential infrastructure for sustaining the expanding use of electric bikes as a green means of transportation. IoT-based boosting algorithm to increase efficiency in EV applications is presented by Urooj et al. in [27].

3. Smart Charging System for Green Transportation

3.1. Background

Big modern cities of the world marked topped in the ranking of the harmful air quality index. To reduce pollution, green transportation such as electric trains, electric buses, EVs, and electric bikes should be promoted. The desire to automate processes has a big impact on the overall efficiency of a system. The basic requirement for process automation is that physical objects can communicate with each other and perform ubiquitous sensing to make independent decisions. For the last decade, this has been achieved by equipping the objects with appropriate sensors, microcontrollers, and communication protocols, evolving a new field of research termed as Internet of Things (IoT) [28, 29]. The IoT concept, hence, aims at interconnecting everyday objects including not only devices but also stuff like food, clothing, chairs, animals, trees, water, etc., to the Internet to enable seamless communications and automatic remote process management. This paradigm shift from manual to intelligent fully autonomous systems finds its application in enormous fields including but not limited to healthcare, transport, agriculture, environmental protection/monitoring, homes, industry, tourism, and surveillance systems.

3.2. IoT Architecture

IoT architecture mainly consists of sensors, actuators, processors, and communication modules. Sensor nodes collect the required data which is then forwarded to the control center or cloud through the gateway node. In the cloud, the data can be stored using cloud storage services or processed and analyzed with data analytics, and further processing decisions can be made using any heuristic or artificial intelligence technique. After the decision is made, the command to be executed is then forwarded to the actuator deployed in the IoT system. Actuators take electrical input to produce the desired output [30]. The communication technology to be used in an IoT system depends upon the nature of the IoT application. For instance, short-range high data rate communication can be achieved using Wi-Fi. Other options available in the category of short-range low power-consuming technologies are Zig-bee and Bluetooth but have low data rates as compared to Wi-Fi. On the other hand, for wide area coverage, technologies such as LoRa, GSM, GPRS, and LTE can be used.

3.3. IoT Application in Electric Vehicle Charging Stations

As mentioned earlier, IoT also finds its application in the transport sector. IoT solutions are being adopted by transportation companies and agencies all over the world to increase passenger and driver comfort as well as safety, performance, and cost-effectiveness. Some of the major applications include but are not limited to traffic management, self-driving cars, automated toll system, and electric vehicle management systems. IoT seems to play a very vital role in the EV industry by providing, on the run, information about acceleration, mileage, charging, battery temperature, available charging stations, etc. Real-time monitoring of battery parameters plays a very important role in the overall performance and smooth operation of an electric vehicle (EV) and its charging stations [31]. Once fully charged, the amount of energy supplied by the battery to the vehicle gradually decreases. Therefore, to get the best performance and avoid any unpleasant incident, physical parameters such as voltage and current can be used to measure the state of charge (SoC) of the battery. SoC reading can help in avoiding battery overcharging or deep discharging scenarios, which, if not averted leads to rapid aging of the battery [32]. Moreover, voltage and current can also be used to measure the energy being supplied to the vehicle given the input. If the output does not match the predicted energy output value, the battery can be monitored for fault diagnosis. By further manipulating the same parameters, the power drawn from the charging station can also be calculated to estimate the charging price. Considering the profound benefits of live battery state monitoring, this work aims to provide an IoT-based solution for battery health monitoring by integrating multiple sensors and cloud servers.

4. IoT-Based E-Bike Charging System Design Consideration

The core smart E-bike charging system is designed for 48-volt E-bike batteries which can be powered by an available E-bike charging port. Each charging station has 4 multiple charging modules or ports of nearly 100 watts each. Any efficient solar panel rated 500 W and above can be used as a power source with maximum power point tracking (MPPT) and DC/AC converter during sun hours. The source of input is AC power which is integrated with both the main grid and renewable sources. The renewable integrated AC source provides a cheap charging rate during the availability of natural sources such as sunlight and wind. An isolated DC-DC converter uses a high-frequency transformer (HFT), power MOSFETs, and control IC for proper charging current. Voltage and current sensors monitor the real-time energy flow of the system which will be useful to take necessary actions with the help of the controller. Smart IoT-based systems monitor different parameters such as the battery state of charge (SoC), charging time, cost of charging Rs/kWh, and thermal and health conditions with the help of a sensor network. It will make remarkable improvements in machine-to-machine communications and will improve real-time two-way communications to meet the needs of security, quality, and authenticity of service.

The schematic arrangement of the IoT-based E-bike charger prototype module with a detailed layout is shown in Figure 3. The charger module has two power conversion stages at first, input AC power is converted to DC using a full bridge rectifier, and at second stage, switch mode power supply (SMPS)-based DC/DC converter provides the required charging voltages. The power diodes (D1-D4) perform the rectification process, and then DC bus capacitor C1 is used to achieve smooth DC voltages for the further conversion process. The power MOSFET generates pulses at the primary side of the transformer with a snubber circuit to improve the performance. The Schottky diodes (D5, D6) are used to handle high switching frequency with low forward voltage drop. The sensors measure the charger parameters for effective feedback processing with the help of a NodeMCU controller and multiplexer (MUX) IC. The 48 V E-bike is charged by the DC charging system using current-mode regulated flyback converters (recommended for 100 W SMPS). This E-bike battery charging system consists of sensors and controllers to measure and perform necessary actions. IoT uses wireless communication protocols to connect sensor information to master datasets. The parameters data are observed and shared with the service provider (data server) and customer. The design specification of the prototype model is listed in Table 1.

[figure(s) omitted; refer to PDF]

Table 1

Specifications of smart E-bike charger prototype.

Model parameters	Values
Input supply ($V_{\text{ac}\_\text{rms}}$	220 Vrms, 50 Hz
Rated power (P)	100 W
Output voltage ($V_o$)	54 V
Maximum current ($I_{\max }$)	2 A max
Battery pack (lithium-ion)	48 V, 3 Ah
Switching frequency (f)	65 kHz
Time period (T)	15.34 μs
Input ripple voltage $V_{r\_\text{in}}$	1 V
Output ripple voltage ($V_{r\_\text{out}}$	0.1 V

4.1. Power Converter Design

100 W SMPS-based power converter should be able to charge 48-volt E-bike batteries with nearly 2 A current. The optimum and economical topology for 100 W SMPS is a flyback DC-DC converter. The switching frequency of 65 kHz is selected by using appropriate electronics components used in the current control system (feedback control). The input supply voltage is 220 Vrms, and the minimum DC bus voltage with 1 V ripple $V_{r\_\text{in}}$ becomes [33]$$\begin{array}{}\text{(1)}& V_{\text{DC}\_\text{in}}=V_{\text{ac}\_\text{rms}}\sqrt{2}-V_{r_{\text{in}}}\approx 310\mathrm{V}.\end{array}$$

The other important parameters for the converter designed are peak current ($I_{\text{pk}}$), average trapezoidal current ($I_{\text{tavg}}$), duty cycle (D), magnetic inductances ($L_p\&L_s$), and desire turn ratio (n) for a high-frequency transformer (HFT). The required output voltage ($V_o$ and current $I_o$ are related to input parameters in the following equations [34]:$$\begin{array}{c}\text{(2)}& I_o=I_{\text{dc}}=\mathrm{D}{\mathrm{I}}_{\text{tavg}}=D\frac{I_{\text{pk}+I_{\min }}}{2}=\frac{\mathrm{D}{\mathrm{I}}_{\text{pk}}}{2},V_o=V_{\text{dc}}=\frac{V_{\text{DC}\_\text{in}}}{n}\times \frac{D}{1-D}.\end{array}$$

The turn ratio for desired charging voltages of 54 V is approximately 3, selected from using the above equation (reducing the turn ratio value decreases the peak current level). For maximum 2 A load current (D ≈ 0.4), peak secondary current $I_{\text{sp}}$ should be nearly 10 A; thus, the primary peak $I_{\text{pp}}$ becomes 3.34 A. Primary and secondary inductances are calculated as follows:$$\begin{array}{c}\text{(3)}& L_p=\frac{V_{\text{DC}\_\text{in}}}{n}\times \frac{V_{\text{DC}\_\text{in}}\text{DT}}{I_{\text{pp}}}\approx 0.6\text{\hspace{0.17em}mH},L_s=\frac{L_p}{n}\approx 0.2\text{\hspace{0.17em}mH}.\end{array}$$

Output filter capacitor values are selected by the required voltage ripple at the output port of the charger. For an output ripple of 0.1 V, the required capacitance is [35]$$\begin{array}{}\text{(4)}& C_{\text{out}}C4=\frac{I_o{T-T}_{\text{off}}}{V_{r_{\text{out}}}}=\frac{I_o{T}_{\text{on}}}{V_{r_{\text{out}}}}=185\mu \mathrm{F}\approx 200\mu \mathrm{F}.\end{array}$$

4.2. High-Frequency Transformer Design

HFT is designed to handle more volts safely and accurately, converting high voltage and current levels between coils by magnetic induction. This will be designed by calculating the turn ratio, core parameter, core structure, and assembly of the transformer [36]. On the secondary side of the HFT, Schottky diode will be used with negligible reverse recovery losses. The primary short circuit current is nearly $I_{{\text{sc}}_{\mathrm{p}}}\approx I_{\text{pp}}$ 3.3 A. The full load rms current $I_{\text{FL}}$, required core area product ($A_p$, and turn ratio are calculated [34, 37] as$$\begin{array}{c}\text{(5)}& I_{\text{FL}}\text{rms}={\frac{{D{I}_{P_P}}^2}{3}}^{1/2}=1.2\mathrm{A},A_p=A_w{A}_c={\frac{\mathrm{L}{\mathrm{I}}_{\text{sc}}}{B_{\max }}\times \frac{I_{\text{FL}}}{K_1}}^{4/3}=1.376{\text{cm}}^4,\\ N_p=\frac{V_{\text{in}}\times {10}^4}{\text{Kf}{B}_m{A}_C}=50.2\approx 50\text{\hspace{0.17em}turns},\\ N_s=\frac{N_p}{n}=16.67\approx 18\text{\hspace{0.17em}turns}.\end{array}$$

EI core (EI 33/23/13) is selected for winding with an area product of 1.59 cm⁴ and a core area of $A_c$ = 1.18 cm². The summarized parameters of the power converter are mentioned in Table 2.

Table 2

Designed parameters for SMPS-based E-bike charger.

Parameter		Value
$B_{\max }$	Maximum magnetic flux density	0.2 T (tesla)
$K_1$	Magnetic core constant	0.0085
$K_t$	Waveshape constant	4 (for square)
$N_p,N_s$	High-frequency transformer (HFT) turn ratio	50 : 18
$L_p,L_s$	Primary and secondary inductances	0.6 mH, 0.2 mH
$A_p$	Transformer area product	1.59 cm4
$A_c$	Core area	1.18 cm2
$V_{\text{dc}\_\text{in}}$	Bus voltages	310 V·max

4.3. IoT-Based System Design

4.3.1. IoT-Based Four Layered Architecture for Parameter Monitoring

The proposed system intends to provide real-time EV battery health monitoring at the charging station to both the customer and the supplier. To achieve this goal, the system comprises four-layered architecture, as mentioned in Figure 4. The first layer, which is the physical layer, includes an electric bike, electric bike charger, NodeMCU, voltage, and current sensor. The electric bike uses a rechargeable lithium-ion battery instead of a combustion engine. Electric bike chargers use both direct and solar-powered systems as their source.

[figure(s) omitted; refer to PDF]

Voltage and current sensors are used to determine the amount of voltage available and the flow of current, respectively. The processing of data to calculate SoC and charging price is done in the second layer labeled as the data access and processing layer. The third layer is the communication layer which uses 802.11 b/g/n for transmitting data collected from the sensors to the ThingSpeak cloud server. ThingSpeak is an IoT-based cloud analytics platform service where sensor data can be sent to the cloud to aggregate, visualize, and analyze real-time data. To use ThingSpeak, it is required to sign up and create a channel. Once a channel is created, data can be sent to the cloud for live visualization. The fourth layer is the interface layer which will enable the customer as well as the service provider to view all the parameters using the ThingSpeak web interface or through the mobile application. NodeMCU (node microcontroller unit) is an IoT development platform built around ESP8266 microcontroller, with an operating voltage of 3.3 V and a flash memory of 4 MB. Detailed specifications of NodeMCU are given in Table 3.

Table 3

NodeMCU technical specifications.

Component	Detail
NodeMCU	Model: clone LoLin
ESP-8266	32 bit processor
Clock speed	20 MHz
Operating voltage	3.3 V
Digital I/O pins	11
Analog input pin	1
WiFi built-in	802.11 b/g/n
ADC resolution	10 bit

4.3.2. IoT-Based Parameter Monitoring

To validate the aboveproposed system architecture, a prototype model is implemented (detail discussion in Section 5.2). In the implemented prototype model, battery voltages are measured using a resistive network voltage sensor (0–100 V). The resistive voltage sensor works on the principle of the voltage divider rule. The output of the voltage sensor can be expressed as$$\begin{array}{}\text{(6)}& V_{\text{out}}=\frac{R_m}{R_m+R_f}\times V_{\text{ref}},\end{array}$$where $R_m$ is 7.5 k and $R_f$ is 142.5 k in the given prototype model. The current drawn from the charger to the bike battery is observed using a Hall effect-based current sensor, ACS712. The sensor is available in the market in three different ratings. In the proposed system, ACS712 with current measuring capability lying in the range +5 A and sensitivity 185 $\text{mV}/\mathrm{A}$ is used. The current sensor is connected in series with the charger. The output from both sensors is then given as input to the nodeMCU for further processing. NodeMCU has 11 digital I/O pins and only one analog pin. However, the output from both sensors is analog. Therefore, both sensors cannot be directly connected to the NodeMCU. For that reason, CD74HC4067, a 16-channel analog multiplexer chip, is used to increase the analog inputs for NodeMCU. CD74HC4067 module can operate on voltages between 2 and 6 V DC allowing it to be easily used with NodeMCU. The output from the sensors is first given as input to the multiplexer module and the select line logic is used to first send the current value and then the voltage value to NodeMCU. In NodeMCU, the output from the current sensor is further processed to exact the current value using the following equation:$$\begin{array}{}\text{(7)}& I=\frac{{\text{senso}\mathrm{r}}_{\text{value}}/1024-0.33\ast 3.3-3.3/2}{0.185}.\end{array}$$

In the above equation, to convert the read sensor value to the voltage factor, $3.3/1024$ is used. The achieved voltage value is then subtracted from the quiescent output voltage given as $3.3/2$. The quiescent output voltage is the voltage of a device when the applied current is zero. The factor 0.185 V is the voltage sensitivity of the current sensor which is a rise in output voltage when 1 A current flows at the input. The output voltage, $V_{\text{out}}$, received by NodeMCU from the voltage sensor is further manipulated to measure the actual input voltage ($V$) value using the equation given as follows:$$\begin{array}{}\text{(8)}& V=\frac{R_1+R_2}{R_2}{V}_{\text{out}}.\end{array}$$

The measured voltage and current readings are then used to calculate the power being consumed by using equation (9). The percentage of charge being stored in the battery is further used to calculate the state of charge (SoC) of the battery. Assuming, the EV is charged for “t” hours and the price per unit is ^′$P_r$^′ and then the total charging price will be$$\begin{array}{}\text{(9)}& \text{Power}=\text{charging\hspace{0.17em}voltages\hspace{0.17em}}\mathrm{V}\times \text{charging\hspace{0.17em}current\hspace{0.17em}}\mathrm{I},\text{(10)}& \text{Total\hspace{0.17em}Charging\hspace{0.17em}Price}=\frac{\text{Power}\times t}{1000}\times P_r.\end{array}$$

All the parameter analyses and calculations are done in NodeMCU using Arduino IDE. After the calculation of the parameters, the data are sent to the ThingSpeak cloud for live monitoring by the user and service provider. To send the data to the ThingSpeak cloud, two libraries are to be included: the first is ESP8266WiFi which is used to connect to the available WiFi network, and the other is the ThingSpeak library to write data to the ThingSpeak server. From the ThingSpeak library, the writeField function is used to send one value of data at a time. The arguments passed to the writeField function are channel number, field, value, and writeAPIKey “writeField (channelNumber, field, value, writeAPIKey)” where channelNumber is the number of the channels present on the ThingSpeak to which data has to be sent. Each channel contains 8 different fields which can be used to observe 8 different values. For example, voltage values can be plotted to field 1 of the channel and current values to field 2 and so on. The third argument is the reading of a specific parameter, and the last is the application user interface key which must be mentioned here to write data on that channel.

5. Results and Discussion

5.1. Simulation Analysis

MATLAB Simscape physical modeling is a powerful tool to validate the hardware design and real-time analysis of the system [38–40]. Different SMPS topology converter behavior can be designed using the MATLAB environment [41]. The detailed and wide-ranging simulation model of an IoT-based E-bike charger is shown in Figure 5(a). The simulation is designed on the Simscape physical modeling tool (MATLAB) using design calculation performed in Section 2. The Simscape components of semiconductor devices are used to model charger circuits. The power diodes and passive elements are placed as per calculated values. The N-Channel MOSFET from the Simscape electric library is used to model the power MOSFET. The gate driver block provides the required voltage level for optimum performance of the transistor switch. The current controller subsystem provides the gate pulses to control the charging current of the designed charger.

[figure(s) omitted; refer to PDF]

The complete EV charger model contains different blocks and subsystems, such as AC power, SMPS (flyback topology) power converters, HFT, feedback control, driver circuits, physical components, and battery model Simulation analysis is performed using the Euler method by setting a sampling rate of 1e-7 seconds. In the simulation model, the virtual observing system is designed to monitor the charging paraments which reflect the real-time parameter observing system. The charging response of voltages and current is helpful to verify the theoretical calculation of charger design as shown in Figures 5(b) and 5(c).

In smart charging stations, prices and different parameters can be selected easily by the service provider. Figure 6 shows the interface provided to the end user. Figure 6(a) presents the charging voltages response. Figure 6(b) shows the battery charging current. Figure 6(c) presents the battery charging price. Figure 6(d) presents the state of charge, and Figure 6(e) shows the temperature. The results are generated by using the data collected from the sensors which are included in the first layer of the proposed system’s architecture. After processing the data in the second layer, it is forwarded to the ThingSpeak web application (last layer) by using the communication layer access protocol.

[figure(s) omitted; refer to PDF]

5.2. Hardware Validation of Prototype Model

A hardware prototype model of an electric bike charging station consists of a charging circuit, a control system, an E-bike battery, a controller, and smart features. The theoretical and simulation analysis discussed in Sections 4 and 5.1 is verified using an experimental setup. Using a bridge rectifier circuit rating of (1 A, $V_B$ > 300 V), input AC power is converted into DC level. A flyback DC/DC power converter (SMPS) is used due to its simplicity and low cost. With the help of an EI-33 magnetic ferrite core, the high-frequency transformer is designed as per calculations in Section 4.2 with a bobbin size of 32 mm. 50 : 18 turns of wire size 27 (AWG) are used for winding HFT for required charging voltages. The prototype battery pack is designed using 3.7 V, 18650 (3 Ah) lithium cells with a nearly 0.5 C charging rate. Complete hardware specifications with labels as per Figure 3 are mentioned in Table 4.

Table 4

Hardware specifications.

Component	Specifications
Bridge rectifier (diodes D1–D4)	400 V, 1 A
Schottky diodes (D5, D6)	3 A
MOSFET (PTA20N65)	650 V, 20 A
Current mode IC	UC3842
Resistances (R1, R2)	R1 = 10 kΩ, R2 = 47 Ω
Optocoupler	PC 817
Controller	NodeMCU
Analog multiplexer	74HC4067 (16-channel)
Current sensor	ACS712ELCTR-05B-T
Transformer core	EI 33/23/13
Capacitance (C1-C4)	C1 = 120 μF (450 V)
	C2 = C3 = 470 pF
	C4 = 200 μF

In the hardware design model, the UC3842 IC is used for frequency generation and current control in a designed E-bike battery charger. The UC3842 IC is a specialized design for efficiency, reliability, precise power management, current sensing, feedback loop stability, and cost-effectiveness in the system. The UC3842 is better suited for handling the power levels typically required in battery charging applications. The feedback control system mentioned in the schematic diagram consists of low-cost current mode PWM controllers IC (UC3842). The IC has a configurable built-in oscillator as shown in Figure 7.

[figure(s) omitted; refer to PDF]

The desired frequency of 65 kHz is achieved by selecting the appropriate timing resistor ($R_t$ = 12 kΩ) and capacitor ($C_t$ = 2.2 nF) values, according to the following equation [42]:$$\begin{array}{}\text{(11)}& F=\frac{1.72}{R_t{\times C}_t}.\end{array}$$

The controller in our model is used for user interface, communication, monitoring, and overall system control. The Mux (74HC4067) is used to route multiple input signals (sensors) to a single output pin based on control signals, as NodeMCU(ESP8266) has only one analog pin. The experimental analysis of the prototype model is performed in the laboratory with measurement tools as shown in Figure 8(a). The digital multimeter shows the output charging current of the battery, and the oscilloscope represents the charging voltage behavior in Figure 8(b). The IoT module kit connected to an analog multiplexer integrated with sensors is shown in Figure 8(c).

[figure(s) omitted; refer to PDF]

The simulation and hardware response of the E-bike battery charging system is approximately the same for the prototype model as shown in Figures 5(b) and 7(b), respectively. The higher switching frequency of HFT is obtained by selecting a transistor (MOSFET) having a required switching time with breakdown voltages of twice of maximum input DC level. The optocoupler gets the signal from an output of the SMPS charger and gives this signal to the IC (UC3842) to lock the voltage and control the charging current. Snubber circuits are essential to protect the devices from overvoltage spikes, and they are placed to protect the charging circuit. The battery charging system consists of sensors and a controller (Node MCU) for smart operations. The voltage sensor is designed using a simple voltage divider bias technique. At the output stage, Schottky diodes are used to perform rectification at a high frequency. The charging module with detailed core dimensions and winding is shown in Figure 9. Figure 9(a) shows the charger module, Figure 9(b) presents the HFT core dimensions, Figure 9(c) shows the EI port Bobbin, and Figure 9(d) shows the HFT winding.

[figure(s) omitted; refer to PDF]

Figure 10 shows the integrating ThingSpeak with hardware prototype. Figure 10(a) shows the battery charging current, Figure 10(b) provides the charging voltage, Figure 10(c) shows the state of charge, and Figure 10(d) presents the battery charging price.

[figure(s) omitted; refer to PDF]

5.3. Economic Analysis and Discussions

The simulation and experimental analysis of the proposed IoT-enabled E-bike charger validates the design procedure with a smart interface. Detailed mathematical equations help design charging modules for specific electric vehicle applications. Simulation analysis is useful to evaluate the circuit performance on a wide range of parameters. Analysis can be used to improve the charger’s performance under different battery and thermal conditions.

Real-time monitoring systems provide the facility to access the relevant parameters remotely and easily. They improve the charging cost reliability, timely maintenance, and system health conditions. Data cloud stats can help design better charging algorithms, battery alerts, mileage improvement mechanisms, security and safety issues, economic analysis, etc. The detailed design procedure and simulation analyses help develop different EV chargers. The design procedures are useful for selecting filter values, switch frequencies, transformer parameters, and the power device’s current ratings. The smart economical charging station is specially designed for public and private organizations such as industries, universities/colleges, shopping malls, and hospitals to promote a green transportation system. The system provides numerous advantages to E-bike users and the environment.

The smart hardware prototype module is compact and economical as compared to the available E-bike charger in the market with the same specification as shown in Table 5 (few standard chargers are compared). The experimental setup illustrates the component-wise detailed procedure to design the hardware model. The proposed research successfully designed the IoT-based E-bike charging station having multiple charging ports for users. Smart systems are reliable, secure, and economical for both the service provider and the customer.

Table 5

E-bike chargers’ comparison (110/220 V, 50/60 Hz input supply).

Manufacturer	Specifications	Cost	Smart	Features
Lewe electronics [43]	54 V, 2 A	$24.99	None	Safety protections, li-ion battery charger, compact, electric bike, scooter, bicycle
Greenergy [44]	54 or 48 V, 2 A	$26	None	Easy to care, 54/42 standard sets, lithium battery, EU US UK plug standards
EVBIKE [45]	48 V, 2 A	$49	None	Safety protections, 36/48 volts, less weight, cooling fan, li-ion battery charger, aluminum box
MATE [46]	54.6 V, 2 A	$108	None	Li-ion charger, compact, 1.2 kg, EU/USA plug, cooling fan
Proposed	54 V, 2 A	$19.6	Yes	Safety protections, compact, li-ion charger, multiple parameter analyses, wireless monitoring, cloud data, security, diagnostics alerts

E-bike charging stations charge less per unit (kWh) cost as compared to high fuel price per liter, making it affordable for electric bike users. Additionally, charging them with renewable sources further decreases their carbon footprint. The availability of charging stations promotes the use of electric vehicles and decreases the charging anxiety of users. Also, it promotes the local economy, business opportunities, and degradation of oil import bills.

The smart economical E-bike charging station is the future demand for electric vehicle transportation systems. The proposed IoT-based economical charger is nearly $20 per module which is acceptable for any organization to install charging infrastructure to promote a green and cheap conveyance system. Full e-bike battery recharging cost is nearly 4 to 5 cents (for the unit cost of ≈ $0.1/kWh) which is also suitable for the transportation system of low/middle economic countries. Overall, an IoT-based economical bike charging system offers countless benefits to users, service providers, and the country. The system improves user experience, security, and safety, making it an essential component of a sustainable transportation system.

5.4. Future Research Directions

The research presents a comprehensive design analysis of smart E-bike charger design for the targeted two-wheel vehicles. The paper discusses both the hardware and software designing aspects of the system. For the continuation of the research, further issues can also be addressed. Future research investigations can focus on the effect of different switching frequencies on the overall system in terms of harmonics, filter size, and magnetics. Further, the power converter efficiency, performance, and thermal consideration under different frequencies and currents will also be effective research aspects of the topic.

Advanced artificial intelligence (AI)-based algorithms can be applied to optimize energy efficiency in electric bike charging [47]. These algorithms can also improve battery health and the mileage range of EVs. Future studies might explore the social and economic effects of the widespread embracing of IoT-based smart electric bike charger systems.

6. Conclusion

The development of a public economic charging station is a key element for promoting electric bikes. The proposed research successfully develops an economical electric bike charging structure with smart features. Detailed hardware prototype design methodology is helpful to construct a specific charger module for EV applications. The IoT module senses the real-time parameter of the charging station and sends it to an online monitoring system via a Wi-Fi wireless network. The smart system is useful for estimating charging cost, battery health, fault diagnostics, and maintenance alerts and improving overall performance, which will improve the reliability of the E-bike charge. Flyback DC/DC power converter topology-based system is useful for designing simple, low-cost, and compatible E-bike chargers for users. Simulation and experimental results successfully validate the design procedure of a smart economical charger. The smart economical E-bike charging station promotes the two wheels’ intracity transport system which improves the alarming situation of pollution in big cities. The hardware model will be helpful for EV charging industries to introduce their smart economical chargers to promote green transportation to the public. Further, renewable integration reduces the power load burden on local grid stations which will also be helpful to combat the energy crises.

References

[1] G. Town, S. Taghizadeh, S. Deilami, "Review of fast charging for electrified transport: demand, technology, systems, and planning," Energies, vol. 15 no. 4,DOI: 10.3390/en15041276, 2022.

[2] T. Ilahi, T. Izhar, S. M. Qaisar, U. T. Shami, M. Zahid, A. Waqar, A. Alzahrani, "Design and performance analysis of ultra-wide bandgap power devices-based EV fast charger using Bi-directional power converters," IEEE Access, vol. 11, pp. 25285-25297, DOI: 10.1109/ACCESS.2023.3255780, 2023.

[3] S. Chakraborty, H. N. Vu, M. M. Hasan, D. D. Tran, M. E. Baghdadi, O. Hegazy, "DC-DC converter topologies for electric vehicles, plug-in hybrid electric vehicles and fast charging stations: state of the art and future trends," Energies, vol. 12 no. 8,DOI: 10.3390/en12081569, 2019.

[4] T. Gnann, S. Funke, N. Jakobsson, P. Plötz, F. Sprei, A. Bennehag, "Fast charging infrastructure for electric vehicles: today’s situation and future needs," Transportation Research Part D: Transport and Environment, vol. 62, pp. 314-329, DOI: 10.1016/j.trd.2018.03.004, 2018.

[5] S. Ditze, S. Ehrlich, N. Weitz, M. Sauer, F. Aßmus, A. Sacher, C. Joffe, C. Seßler, P. Meißner, "A high-efficiency high-power-density SiC-based portable charger for electric vehicles," Electronics, vol. 11 no. 12,DOI: 10.3390/electronics11121818, 2022.

[6] cleanenergyministerial, "EV30@30 Campaign Clean Energy Ministerial," 2023. https://www.cleanenergyministerial.org/initiatives-campaigns/ev3030-campaign/

[7] T. Ilahi, T. Zahid, M. Zahid, M. Iqbal, A. Sindhila, Q. Tahir, "Design parameter and simulation analysis of electric bike using Bi-directional power converter," Proceedings of the 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE),DOI: 10.1109/ICECCE49384.2020.9179285, .

[8] A. Singh, S. S. Shaha, N. P. G, Y. R. Sekhar, S. Saboor, A. Ghosh, "Design and analysis of a solar-powered electric vehicle charging station for Indian cities," World Electric Vehicle Journal, vol. 12 no. 3,DOI: 10.3390/wevj12030132, 2021.

[9] R. Collin, Y. Miao, A. Yokochi, P. Enjeti, A. Von Jouanne, "Advanced electric vehicle fast-charging technologies," Energies, vol. 12 no. 10,DOI: 10.3390/en12101839, 2019.

[10] E. Ucer, I. Koyuncu, M. C. Kisacikoglu, M. Yavuz, A. Meintz, C. Rames, "Modeling and analysis of a fast charging station and evaluation of service quality for electric vehicles," IEEE Transactions on Transportation Electrification, vol. 5 no. 1, pp. 215-225, DOI: 10.1109/TTE.2019.2897088, 2019.

[11] H. Chen, Z. Hu, H. Luo, J. Qin, R. Rajagopal, H. Zhang, "Design and planning of a multiple-charger multiple-port charging system for PEV charging station," IEEE Transactions on Smart Grid, vol. 10 no. 1, pp. 173-183, DOI: 10.1109/TSG.2017.2735636, 2019.

[12] S. Verma, S. Mishra, A. Gaur, S. Chowdhury, S. Mohapatra, G. Dwivedi, P. Verma, "A comprehensive review on energy storage in hybrid electric vehicle," Journal of Traffic and Transportation Engineering, vol. 8 no. 5, pp. 621-637, DOI: 10.1016/j.jtte.2021.09.001, 2021.

[13] S. Mishra, S. Verma, S. Chowdhury, A. Gaur, S. Mohapatra, G. Dwivedi, P. Verma, "A comprehensive review on developments in electric vehicle charging station infrastructure and present scenario of India," Sustainability, vol. 13 no. 4,DOI: 10.3390/su13042396, 2021.

[14] S. Verma, G. Dwivedi, P. Verma, "Life cycle assessment of electric vehicles in comparison to combustion engine vehicles: a review," Materials Today: Proceedings, vol. 49, pp. 217-222, DOI: 10.1016/j.matpr.2021.01.666, 2022.

[15] S. Verma, G. Dwivedi, A. Zare, P. Verma, "Life cycle assessment of greenhouse gas emissions of electric and internal combustion engine vehicles in India," SAE Technical Paper Series, vol. 12,DOI: 10.4271/2022-01-0749, 2022.

[16] H. H. Coban, W. Lewicki, E. Sendek-Matysiak, Z. Łosiewicz, W. Drożdż, R. Miśkiewicz, "Electric vehicles and vehicle–grid interaction in the Turkish electricity system," Energies, vol. 15 no. 21,DOI: 10.3390/en15218218, 2022.

[17] D. Savio Abraham, R. Verma, L. Kanagaraj, S. R. Giri Thulasi Raman, N. Rajamanickam, B. Chokkalingam, K. Marimuthu Sekar, L. Mihet-Popa, "Electric vehicles charging stations’ architectures, criteria, power converters, and control strategies in microgrids," Electronics, vol. 10 no. 16,DOI: 10.3390/electronics10161895, 2021.

[18] H. M. Al-Kadhim, H. S. Al-Raweshidy, "Energy efficient and reliable transport of data in cloud-based IoT," IEEE Access, vol. 7, pp. 64641-64650, DOI: 10.1109/ACCESS.2019.2917387, 2019.

[19] J. Zhang, W. Zhang, J. Wang, J. Feng, Z. Gao, S. Zheng, "Rechargeable battery cabinet deployment for public bike system," IEEE Transactions on Intelligent Transportation Systems, vol. 23 no. 11, pp. 20309-20322, DOI: 10.1109/TITS.2022.3180079, 2022.

[20] statista, "Global e-bike market forecast 2030 Statista," 2023. https://www.statista.com/statistics/1261084/global-e-bike-market-forecast/

[21] S. Mishra, G. Dwivedi, S. Upadhyay, A. Chauhan, "Modelling of standalone solar photovoltaic based electric bike charging," Materials Today: Proceedings, vol. 49, pp. 473-480, DOI: 10.1016/j.matpr.2021.02.738, 2022.

[22] S.-K. Lim, H.-S. Lee, H.-R. Cha, S.-J. Park, "Multi-level DC/DC converter for E-mobility charging stations," IEEE Access, vol. 8, pp. 48774-48783, DOI: 10.1109/ACCESS.2020.2977663, 2020.

[23] C. Song, H. Kim, D. H. Jung, J. J. Kim, S. Kong, J. Kim, S. Ahn, J. Kim, J. Kim, "Low EMF and EMI design of a tightly coupled handheld resonant magnetic field (HH-rmf) charger for automotive battery charging," IEEE Transactions on Electromagnetic Compatibility, vol. 58 no. 4, pp. 1194-1206, DOI: 10.1109/TEMC.2016.2557842, 2016.

[24] C. C. Liao, M. S. Huang, Z. F. Li, F. J. Lin, W. T. Wu, "Simulation-assisted design of a bidirectional wireless power transfer with circular sandwich coils for E-bike sharing system," IEEE Access, vol. 8, pp. 110003-110017, DOI: 10.1109/ACCESS.2020.3000564, 2020.

[25] Z. Zhang, C. Gercek, H. Renner, A. Reinders, L. Fickert, "Resonance instability of photovoltaic E-bike charging stations: control parameters analysis, modeling and experiment," Applied Sciences, vol. 9 no. 2,DOI: 10.3390/app9020252, 2019.

[26] G. R. Chandra Mouli, P. Van Duijsen, F. Grazian, A. Jamodkar, P. Bauer, O. Isabella, "Sustainable E-bike charging station that enables AC, DC and wireless charging from solar energy," Energies, vol. 13 no. 14,DOI: 10.3390/en13143549, 2020.

[27] S. Urooj, F. Alrowais, Y. Teekaraman, H. Manoharan, R. Kuppusamy, "IoT based electric vehicle application using boosting algorithm for smart cities," Energies, vol. 14 no. 4,DOI: 10.3390/en14041072, 2021.

[28] N. Hossein Motlagh, M. Mohammadrezaei, J. Hunt, B. Zakeri, "Internet of things (IoT) and the energy sector," Energies, vol. 13 no. 2,DOI: 10.3390/en13020494, 2020.

[29] O. H. Kombo, S. Kumaran, A. Bovim, "Design and application of a low-cost, low- power, LoRa-GSM, IoT enabled system for monitoring of groundwater resources with energy harvesting integration," IEEE Access, vol. 9, pp. 128417-128433, DOI: 10.1109/ACCESS.2021.3112519, 2021.

[30] K. K. Patel, S. M. Patel, "Internet of Things-IOT: definition, characteristics, architecture, enabling technologies, application and future challenges," International Journal of Engineering Science and Computing, vol. 6, pp. 6122-6131, 2016.

[31] G. F. Savari, V. Krishnasamy, J. Sathik, Z. M. Ali, S. H. E. Abdel Aleem, "Internet of Things based real-time electric vehicle load forecasting and charging station recommendation," ISA Transactions, vol. 97, pp. 431-447, DOI: 10.1016/j.isatra.2019.08.011, 2020.

[32] R. Suresh Kumar, P. K. Rajesh, J. Joys Nancy, S. Abirami, K. Vishnu Murthy, "IoT-based monitoring and management of electric vehicle charging systems for DC fast charging facility," Internet of Things for Industry 4.0: Design, Challenges and Solutions, pp. 147-159, DOI: 10.1007/978-3-030-32530-5_10, 2020.

[33] Y.-S. Lee, M. H. L. Chow, "7-Diode rectifiers," Power Electronics Handbook, pp. 177-208, DOI: 10.1016/B978-0-12-811407-0.00007-6, 2018.

[34] Inductor, "Inductor and flyback transformer design’-TI.com/inductor-and-flyback-transformer-design-ti-com," 2023. https://pdf4pro.com/view/inductor-and-flyback-transformer-design-ti-com-52aaf4.html

[35] A. Pressman, K. Billings, T. Morey, "3-Half-bridge and full-bridge topologies," 2009. https://books.google.com.pk/books?id=RgV4zQEACAAJ

[36] S. Zhang, D. Chen, B. Bai, "Study of a high-power medium frequency transformer using amorphous magnetic material," Symmetry, vol. 14 no. 10,DOI: 10.3390/sym14102129, 2022.

[37] D. Czarkowski, "10-DC-DC converters," Power Electronics Handbook, 2018.

[38] P. Pena, M. Garcia, A. Tekes, Modeling of Compliant Mechanisms in Matlab Simscape, 2020.

[39] B. Xu, L. Lu, D. Gong, "Research on real-time simulation of power electronic circuits based on Simscape," J. Phys. Conf. Ser., vol. 2196 no. 1,DOI: 10.1088/1742-6596/2196/1/012025, 2022.

[40] M. Brenna, M. Longo, W. Yaïci, "Modelling and simulation of electric vehicle fast charging stations driven by high speed railway systems," Energies, vol. 10 no. 9,DOI: 10.3390/en10091268, 2017.

[41] W.-H. Liao, S.-C. Wang, Y.-H. Liu, "Generalized simulation model for a switched-mode power supply design course using MATLAB/SIMULINK," IEEE Transactions on Education, vol. 55 no. 1, pp. 36-47, DOI: 10.1109/TE.2011.2115243, 2012.

[42] T. Intruments, "UCx84x current-mode PWM controllers," 2024. https://www.ti.com/lit/ds/symlink/uc3842.pdf

[43] amazon, "Charger for 48V electric Bike.Scooter 48 volt 13S lithium battery pack charger with DC 5.5mm round connector: sports and outdoors," 2023. https://www.amazon.com/Charger-Electric-Bike-Scooter-Lithium-Connector/dp/B0B2DQLC1R

[44] aliexpress, "Electric bicycle battery charger," 2023. https://www.aliexpress.com/item/32951824890.html?spm=a2g0o.productlist.main.13.47313cdaOmt3Uk&algo_pvid=4ce40dbf-28d0-42a7-9a23-0db7d8973a79&algo_exp_id=4ce40dbf-28d0-42a7-9a23-0db7d8973a79-6&pdp_npi=3%40dis%21PKR%2110465.82%218059.72%21%21%21%21%21%402102172f

[45] evbike, "eBike battery charger 48V, 2A | EVBIKE.eu," 2023. https://www.evbike.eu/p/ebike-battery-charger-48v-2a

[46] mate, "2A charger-rca with plug EU-MATE X–MATE bike international," 2023. https://www.mate.bike/en-be/products/2a-charger-rca-with-plug-eu-mate-x?pr_prod_strat=use_description&pr_rec_id=cfdcffe66&pr_rec_pid=8107460526309&pr_ref_pid=8107458986213&pr_seq=uniform&variant=43666002280677

[47] U. Yilmaz, O. Turksoy, A. Teke, "Intelligent control of high energy efficient two-stage battery charger topology for electric vehicles," Energy, vol. 186,DOI: 10.1016/J.ENERGY.2019.07.155, 2019.